Getting started with Deep Learning

1. Get started with DeepSense

Follow all the steps from Getting started. This tutorial assumes you can log on to the DeepSense compute platform and have a version of Anaconda python on your path. We recommend installing Anaconda in your home directory before starting this tutorial (See Installing Software).

2. Prepare Caffe and download Caffe samples to your home directory

(New method)

Activate your anaconda environment. See Installing Software for how to create an environment. We will assume you have created one called "caffe".

conda activate caffe

Add the IBM-AI anaconda channel if you have not done so already

conda config --prepend channels https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/

Install caffe if you have not done so already

conda install caffe

Install the caffe samples

caffe-install-samples

(Old method)

/opt/DL/caffe/bin/caffe-install-samples

3. More information

Go to Caffe's website for tutorials and example programs that you can run to get started. See the following links to a couple of the example programs:

LeNet MNIST Tutorial - Train a neural network to understand handwritten digits.

CIFAR-10 tutorial - Train a convolutional neural network to classify small images.

4. Using another deep learning toolkit such as Tensorflow

(New Method)

• Ensure any Anaconda dependencies are installed
  • for tensorflow, create a new environment and conda install tensorflow</conda>
• Download example notebooks for the deep learning toolkit to your home directory,
  • e.g. git clone https://github.com/aymericdamien/TensorFlow-Examples.git

(Old Method)

• Ensure any Anaconda dependencies are installed
  • for tensorflow, run /opt/DL/tensorflow/bin/install_dependencies
• Source the appropriate toolkit instead of caffe-activate
  • e.g. source /opt/DL/tensorflow/bin/tensorflow-activate
• Download example notebooks for the deep learning toolkit to your home directory,
  • e.g. git clone https://github.com/aymericdamien/TensorFlow-Examples.git

The TensorFlow home page has various information, including Tutorials, How-To documents, and a Getting Started guide.

Additional tutorials and examples are available from the community, for example:

 https://github.com/nlintz/TensorFlow-Tutorials

 https://github.com/aymericdamien/TensorFlow-Examples